
// //:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
// /** @author  John Miller
//  *  @version 1.0
//  *  @date    Wed Aug 26 18:41:26 EDT 2009
//  *  @see     LICENSE (MIT style license file).
//  */

// package scalation.stat

// import scalation.linalgebra.VectorD
// import scalation.math.DoubleWithExp._
// import scalation.random.Quantile.studentTInv


// //:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
// /** This class is used to collect values and compute sample statistics on them
//  *  (e.g., Waiting Time) using the Method of Batch Means (MBM).
//  *
//  *  BatchStatistic has three phases: Initial, Uncorrelated, Precise.
//  *
//  *  Initial Phase: As samples are tallied, the the number of batches (nBatches) 
//  *  remains constant until the lag-1 autocorrelation of the batch means is less 
//  *  than the specified threshold (c).
//  * 
//  *  Uncorrelated Phase: As samples are tallied, 
//  *
//  *  
//  *
//  *  @param name      the name for this statistic (e.g., WatingTime or tellerQ)
//  *  @param k         the initial number of batches 
//  *  @param c         the autocorrelation threshold
//  *  @param t         the relative precision threshold
//  *  @param unbiased  whether the estimators are restricted to be unbiased.
//  */
// class BatchStatistic2 (name: String, private var k: Int = 10, val c: Int = 0.2, 
//                        val t: Int = 0.2, unbiased: Boolean = false) 
//       extends Statistic(name, unbiased) with Error
// {
    
//     // number of batches
//     def nBatches: Int = k

//     // size of batches
//     def batchSize: Int = num / k


//     // 




// } // StatVector2

